language: en
thumbnail: null
tags:
- audio-source-separation
- Source Separation
- Speech Separation
- WHAM!
- SepFormer
- Transformer
license: apache-2.0
datasets:
- WHAMR!
metrics:
- SI-SNRi
- SDRi
pipeline:
- audio source separation
SepFormer trained on WHAMR! (16k sampling frequency)
This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset.
| Release | Test-Set SI-SNRi | Test-Set SDRi |
|---|---|---|
| 30-03-21 | 13.5 dB | 13.0 dB |
Install SpeechBrain
First of all, please install SpeechBrain with the following command:
pip install speechbrain
Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.
Perform source separation on your own audio file
from speechbrain.pretrained import SepformerSeparation as separator
import torchaudio
model = separator.from_hparams(source="speechbrain/sepformer-whamr16k", savedir='pretrained_models/sepformer-whamr16k')
# for custom file, change path
est_sources = model.separate_file(path='speechbrain/sepformer-whamr16k/test_mixture16k.wav')
torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000)
torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000)
Inference on GPU
To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.
Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
Referencing SpeechBrain
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
}
Referencing SepFormer
@inproceedings{subakan2021attention,
title={Attention is All You Need in Speech Separation},
author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong},
year={2021},
booktitle={ICASSP 2021}
}
About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/